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Fault classification in the process industry using polygon generation and deep learning

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Abstract

This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data, thereby supporting accurate “end-to-end learning” in industrial fault classification applications. Moreover, the generated polygons can play a significant role in the interpretation of trained deep learning fault classifiers. The performance of the proposed method was demonstrated using a benchmark dataset in the process industry. It was also tested successfully to classify challenging faults in major equipment in a thermomechanical pulp mill located in Canada. The results of the proposed method show better performance than other comparable fault classifiers.

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Acknowledgements

This work was supported by Natural Resources Canada’s OERD (Office for Energy Research and Development) program.

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Authors and Affiliations

  1. Department of Mathematics and Industrial Engineering, Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal, Québec, H3T 1J4, Canada

    Mohamed Elhefnawy & Mohamed-Salah Ouali

  2. CanmetENERGY, 1615 Lionel-Boulet Blvd, P.O. Box 4800, Varennes, Québec, J3X 1P7, Canada

    Mohamed Elhefnawy & Ahmed Ragab

  3. Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt

    Ahmed Ragab

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  1. Mohamed Elhefnawy
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Correspondence to Ahmed Ragab.

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Elhefnawy, M., Ragab, A. & Ouali, MS. Fault classification in the process industry using polygon generation and deep learning. J Intell Manuf 33, 1531–1544 (2022). https://doi.org/10.1007/s10845-021-01742-x

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